A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

A simple mathematical model for predicting the solid concentration profile in binary‐solid liquid fluidized beds is presented. The main assumption is that the solid concentration distribution follows the logistic function, which is supported by the literature. Various equations have been derived to solve key system quantities (e.g., bed expansion height, length and position of the transition zone). In contrast to previous models that often involve adjustable parameters and strongly rely on the availability of experimental data, the present model only requires inputs of fluid and particle properties, operating conditions, and correlations for dispersion and slip velocity. The results showed that the model is applicable to different binary‐solid systems that have size and/or density differences. The model's capability of predicting the layer inversion phenomenon has also been demonstrated. The model is simple but proves capable of accurately predicting key information for the design, operation and scale up of liquid fluidized beds. © 2016 American Institute of Chemical Engineers AIChE J, 63: 469–484, 2017     

Synthesis, characterization and application of allyl phenol modified amberlite XAD-4 resin for preconcentration and determination of copper in water samples

A new chelating resin was prepared by coupling Amberlite XAD-4 with phenol through an azo spacer, then modified by allyl bromide and characterized (by elemental analysis and IR) and studied for preconcentration of Cu(II) using flame atomic absorption spectrometry (FAAS) for metal monitoring. The optimum pH value for sorption of the above mentioned metal ion was 4.5. The resin was subjected to chemical evaluation through batch binding and column chromatography of Cu(II). The chelating resin can be reused for 15 cycles of sorption-desorption without any significant change in sorption capacity. A recovery of 98% was obtained for the metal ion with 0.5 M HNO₃ as eluting agent. The equilibrium adsorption data of Cu(II) on modified resin were analyzed by Langmuir, Freundlich and Temkin models. Based on equilibrium adsorption data the Langmuir, Freundlich and Temkin constants were determined to be 0.061, 0.193 and 0.045 at pH 4.5 and 25 °C. The method was applied for the copper determination from industrial waste water sample.     

A method for preparation and application of micronized ferrite pigments in anticorrosive solvent-based paints

This work presents a method of preparing single-, double-, and triple-cation ferrite pigments by employing simple chemical techniques to study their corrosion protection properties. The prepared pigments were characterized using X-ray diffraction and scanning electron microscopy. All prepared ferrite pigments were evaluated using ASTM methods. Anticorrosive paint formulations were performed using different prepared pigment loadings. The physico-mechanical and corrosion properties of dry paint films were examined. The tests revealed that the prepared ferrite pigments show excellent anticorrosive behavior, and that the best among them in performance are zinc and zinc magnesium. Calcium, zinc–calcium, and zinc–magnesium–calcium ferrites show better performance in high pigment loadings, while magnesium ferrite pigments show good results only in low pigment loading.     

Mathematical modeling and genetic algorithm optimization of clove oil extraction with supercritical carbon dioxide

In the present study, a mathematical modeling for extraction of oil from clove buds using supercritical carbon dioxide was performed. Mass transfer is based on local equilibrium between solvent and solid. The model was solved numerically, and model estimation was validated using experimental data. For optimization, the clove oil equilibrium constant between solid and supercritical phase was determined by a theoretical method using fugacity concept, consequently the genetic algorithm for obtaining optimal operational conditions was used. The optimal conditions which obtained the highest amount of clove oil were pressure of 10 MPa and temperature of 304.2 K.     

Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

     

Robust image watermarking scheme using bit-plane of hadamard coefficients

Multimedia Tools and Applications - Nowadays, due to widespread usage of the Internet, digital contents are distributed quickly and inexpensively throughout the world. Watermarking techniques can...